Enhancing tutoring systems by leveraging tailored promptings and domain knowledge with Large Language Models

• URL: http://arxiv.org/abs/2505.02849v1
• Date: Fri, 02 May 2025 02:30:39 GMT
• Title: Enhancing tutoring systems by leveraging tailored promptings and domain knowledge with Large Language Models
• Authors: Mohsen Balavar, Wenli Yang, David Herbert, Soonja Yeom,
• Abstract summary: AI-driven tools like ChatGPT and Intelligent Tutoring Systems (ITS) have enhanced learning experiences through personalisation and flexibility.<n>ITSs can adapt to individual learning needs and provide customised feedback based on a student's performance, cognitive state, and learning path.<n>Our research aims to address these gaps by integrating skill-aligned feedback via Retrieval Augmented Generation (RAG) into prompt engineering for Large Language Models (LLMs)
• Score: 2.5362697136900563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in artificial intelligence (AI) and machine learning have reignited interest in their impact on Computer-based Learning (CBL). AI-driven tools like ChatGPT and Intelligent Tutoring Systems (ITS) have enhanced learning experiences through personalisation and flexibility. ITSs can adapt to individual learning needs and provide customised feedback based on a student's performance, cognitive state, and learning path. Despite these advances, challenges remain in accommodating diverse learning styles and delivering real-time, context-aware feedback. Our research aims to address these gaps by integrating skill-aligned feedback via Retrieval Augmented Generation (RAG) into prompt engineering for Large Language Models (LLMs) and developing an application to enhance learning through personalised tutoring in a computer science programming context. The pilot study evaluated a proposed system using three quantitative metrics: readability score, response time, and feedback depth, across three programming tasks of varying complexity. The system successfully sorted simulated students into three skill-level categories and provided context-aware feedback. This targeted approach demonstrated better effectiveness and adaptability compared to general methods.

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